AMD EPYC Zen 4C Power Efficiency

AMD EPYC Zen 4C power efficiency benchmarks by Michael Larabel for a future article.

HTML result view exported from: https://openbenchmarking.org/result/2401147-NE-AMDEPYCZE24&rdt&grr.

Xcompact3d Incompact3d

Input: X3D-benchmarking input.i3d

CloverLeaf

Input: clover_bm16

Timed Linux Kernel Compilation

Build: allmodconfig

LAMMPS Molecular Dynamics Simulator

Model: 20k Atoms

Blender

Blend File: Barbershop - Compute: CPU-Only

OpenRadioss

Model: Chrysler Neon 1M

Timed Gem5 Compilation

Time To Compile

Xmrig

Variant: GhostRider - Hash Count: 1M

Timed LLVM Compilation

Build System: Unix Makefiles

Llama.cpp

Model: llama-2-70b-chat.Q5_0.gguf

Quicksilver

Input: CTS2

OpenFOAM

Input: drivaerFastback, Medium Mesh Size - Execution Time

OpenFOAM

Input: drivaerFastback, Medium Mesh Size - Mesh Time

easyWave

Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400

Timed LLVM Compilation

Build System: Ninja

DuckDB

Benchmark: TPC-H Parquet

Timed Node.js Compilation

Time To Compile

FFmpeg

Encoder: libx265 - Scenario: Video On Demand

FFmpeg

Encoder: libx265 - Scenario: Platform

FFmpeg

Encoder: libx265 - Scenario: Upload

Llama.cpp

Model: llama-2-13b.Q4_0.gguf

Xmrig

Variant: Monero - Hash Count: 1M

Xmrig

Variant: CryptoNight-Femto UPX2 - Hash Count: 1M

Xmrig

Variant: KawPow - Hash Count: 1M

Xmrig

Variant: CryptoNight-Heavy - Hash Count: 1M

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100

Apache IoTDB

Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500 - Client Number: 100

DuckDB

Benchmark: IMDB

OSPRay Studio

Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU

OpenSSL

Algorithm: SHA256

OpenSSL

Algorithm: ChaCha20-Poly1305

OpenSSL

Algorithm: ChaCha20

OpenSSL

Algorithm: AES-256-GCM

OpenSSL

Algorithm: AES-128-GCM

OpenSSL

Algorithm: SHA512

Apache Cassandra

Test: Writes

Blender

Blend File: Pabellon Barcelona - Compute: CPU-Only

RocksDB

Test: Read While Writing

OSPRay Studio

Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU

OSPRay

Benchmark: particle_volume/scivis/real_time

Speedb

Test: Read While Writing

OSPRay

Benchmark: particle_volume/pathtracer/real_time

Llama.cpp

Model: llama-2-7b.Q4_0.gguf

miniBUDE

Implementation: OpenMP - Input Deck: BM2

miniBUDE

Implementation: OpenMP - Input Deck: BM2

Blender

Blend File: Classroom - Compute: CPU-Only

Speedb

Test: Update Random

OSPRay

Benchmark: particle_volume/ao/real_time

OSPRay Studio

Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU

GPAW

Input: Carbon Nanotube

OSPRay Studio

Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU

nginx

Connections: 1000

nginx

Connections: 500

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream

OSPRay Studio

Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU

OSPRay Studio

Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU

OpenVINO

Model: Handwritten English Recognition FP16-INT8 - Device: CPU

OpenVINO

Model: Handwritten English Recognition FP16-INT8 - Device: CPU

Chaos Group V-RAY

Mode: CPU

rav1e

Speed: 10

QuantLib

Configuration: Multi-Threaded

FFmpeg

Encoder: libx265 - Scenario: Live

TensorFlow

Device: CPU - Batch Size: 16 - Model: ResNet-50

CloverLeaf

Input: clover_bm64_short

Blender

Blend File: Fishy Cat - Compute: CPU-Only

IndigoBench

Acceleration: CPU - Scene: Bedroom

Redis 7.0.12 + memtier_benchmark

Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10

Redis 7.0.12 + memtier_benchmark

Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5

VVenC

Video Input: Bosphorus 4K - Video Preset: Faster

easyWave

Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200

Quicksilver

Input: CORAL2 P1

OpenVINO

Model: Face Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Face Detection FP16-INT8 - Device: CPU

IndigoBench

Acceleration: CPU - Scene: Supercar

OSPRay

Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time

PyTorch

Device: CPU - Batch Size: 256 - Model: ResNet-50

Speedb

Test: Random Read

OpenVINO

Model: Person Detection FP16 - Device: CPU

OpenVINO

Model: Person Detection FP16 - Device: CPU

OpenVINO

Model: Machine Translation EN To DE FP16 - Device: CPU

OpenVINO

Model: Machine Translation EN To DE FP16 - Device: CPU

OpenVINO

Model: Person Vehicle Bike Detection FP16 - Device: CPU

OpenVINO

Model: Person Vehicle Bike Detection FP16 - Device: CPU

OpenVINO

Model: Road Segmentation ADAS FP16-INT8 - Device: CPU

OpenVINO

Model: Road Segmentation ADAS FP16-INT8 - Device: CPU

OpenVINO

Model: Face Detection Retail FP16-INT8 - Device: CPU

OpenVINO

Model: Face Detection Retail FP16-INT8 - Device: CPU

OpenVINO

Model: Vehicle Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Vehicle Detection FP16-INT8 - Device: CPU

OSPRay

Benchmark: gravity_spheres_volume/dim_512/scivis/real_time

OpenVINO

Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU

OpenVINO

Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU

OpenVINO

Model: Weld Porosity Detection FP16-INT8 - Device: CPU

OpenVINO

Model: Weld Porosity Detection FP16-INT8 - Device: CPU

Speedb

Test: Read Random Write Random

Timed Linux Kernel Compilation

Build: defconfig

OSPRay

Benchmark: gravity_spheres_volume/dim_512/ao/real_time

OpenSSL

Algorithm: RSA4096

OpenSSL

Algorithm: RSA4096

RocksDB

Test: Random Read

Xmrig

Variant: Wownero - Hash Count: 1M

Blender

Blend File: BMW27 - Compute: CPU-Only

Neural Magic DeepSparse

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

rav1e

Speed: 6

ACES DGEMM

Sustained Floating-Point Rate

Neural Magic DeepSparse

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream